library(readr)
library(tidyverse)
library(forcats)
library(plotly)
library(knitr, warn.conflicts = FALSE, quietly=TRUE)
library(RColorBrewer)
library(stringr)
library(dygraphs)
library(xts)
myPalette <- brewer.pal(8, "YlGn")
vgsales <- read_csv("vgsales.csv")
WorldPopulation <- read.csv("H:/Hochschule/VDA/R_Studio_Default_workspace/API_SP.POP.TOTL_DS2_en_csv_v2_4218816.csv")Das verwendete Dataset des Projekts: https://www.kaggle.com/datasets/gregorut/videogamesales
Dieses Dataset umfasst verschiedene Daten zu Videogamereleases und Sales zwischen 1980 und 2016.
Ideen/Statements:
Bestimmte Entwickler/Publisher häufen sich (Nintendo/EA) Ältere Plattformen/spiele haben mehr verkäufe bzw Wie hat sich die Anzahl der verkäufe im laufe der jahre entwickelt? Genrenentwicklung über die Jahre Welche Spiele/Publisher/Genres in welchen Teilen der welt sich häufen (Nintendo in Asien, Shooter in US/EU) Gibt es Statistische zusammenhänge zwischen einzelnen Faktoren e.g. Genre -> Sales Welche Jahre sind die besten in der Anzahl der releasten games und hängt dies mit den Sales zusammen? (je mehr Total Sales desto mehr Games verkaufen sich) Welche Jahre sind die besten in Anzahl Sales pro game (neuer = besser?) Welche Plattform ist die beste und unterscheidet sich diese nach Region? Gibt es Unterschiede in den Regionen/hängt das mit der Anzahl der Einwohner der Region zusammen? (Asia>US>EU)
Zuerst schauen wir und die Anzahl der Videospiele und die Anzahl der Verkäufe im laufe der Jahre an.
Hierzu verwenden wir Liniendiagramme. Außerdem filtern wir alle Datensätze in denen kein Jahr angegeben ist sowie Datensätze mit den Jahren 2017 und 2020 um Fehlerhafte Daten und unvollständige Daten aus der Liste zu entfernen.
grouped <- vgsales %>%
group_by(Year) %>%
dplyr::summarize(Anzahl =n()) %>% filter(Year!='N/A')%>%filter(Year!=2020) %>% filter(Year!='2017') %>%
as.data.frame()
filtered <- grouped %>% select(Year,Anzahl)
ax <- list(
title = "Year"
)
ay <- list(
title = "Amount"
)
filtered %>%
plot_ly(x = ~Year, y = ~Anzahl, type = 'scatter', mode = 'lines', fill = 'tozeroy') %>%
layout(title="Game Amount from 1980-2016",
xaxis = ax,
yaxis = ay
)grouped <- vgsales %>%
group_by(Year) %>%
dplyr::summarize(gr_sum = sum(Global_Sales)) %>% filter(Year!='N/A')%>%filter(Year!=2020) %>% filter(Year!='2017') %>%
as.data.frame()
filtered <- grouped %>% select(Year,gr_sum)
ax <- list(
title = "Year"
)
ay <- list(
title = "Sales Per Game (in mio)"
)
filtered %>%
plot_ly(x = ~Year, y = ~gr_sum, type = 'scatter', mode = 'lines', fill = 'tozeroy') %>%
layout(title="Sales per Game from 1980-2016",
xaxis = ax,
yaxis = ay
)grouped <- vgsales %>%
group_by(Year) %>%
dplyr::summarize(gr_sum = sum(Global_Sales)/n()) %>% filter(Year!='N/A')%>%filter(Year!=2020) %>% filter(Year!='2017') %>%
as.data.frame()
filtered <- grouped %>% select(Year,gr_sum)
ax <- list(
title = "Year"
)
ay <- list(
title = "Sales per Game"
)
filtered %>%
plot_ly(x = ~Year, y = ~gr_sum, type = 'scatter', mode = 'lines', fill = 'tozeroy') %>%
layout(title="Sales per Game from 1980-2016",
xaxis = ax,
yaxis = ay
)Hier erkennen wir das 2008 und 2009 die Jahre waren in denen die Meisten Games Releast wurden und diese auch die meisten Verkäufe haben. Den größten per Game Verkaufswert gab es allerdings 1984,1985 und 1989. Neuere spiele hingegen verkaufen sich durchschnittlich schlechter was durch das größere Angebot auch zu erwarten ist.
Nun betrachten wir die Anzahl der Videospiele aufgelistet nach Platform.
Hierbei stellt sich die Frage ob ältere Plattformen mehr Spielereleases haben wovon auszugehen ist. Hierzu verwenden wir ein einfaches Balkendiagramm welche eine gute erste übersicht bietet.
grouped <- vgsales %>%
group_by(Platform) %>%
summarize(Anzahl =n())
ordered <- grouped[order(grouped$Anzahl), decreasing = FALSE]
ordered$Platform <- as_factor(ordered$Platform)
ax <- list(
title = "Publisher"
)
ay <- list(
title = "Anzahl"
)
ordered%>%
plot_ly() %>%
add_bars(x=~fct_reorder(Platform,Anzahl, .desc="true"),
y=~Anzahl,
name="Game Amount by Platform") %>%
layout(title="Game Amount by Platform",
xaxis = ax,
yaxis = ay
)Dies lässt sich nicht generell bestätigen aber ein Trend ist definitiv erkennbar.
Nun stellt sich die Frage welche Plattform die meisten Sales hat.
grouped <- vgsales %>%
group_by(Platform) %>%
summarize(sum(Global_Sales)) %>%
rename(
Global_Sales = "sum(Global_Sales)"
)
grouped$Global_Sales<-as_vector(grouped$Global_Sales)
ordered <- grouped[order(grouped$Global_Sales), decreasing = FALSE]
ordered$Platform <- as_factor(ordered$Platform)
ax <- list(
title = "Platform"
)
ay <- list(
title = "Global Sales (in mio)"
)
ordered%>%
plot_ly() %>%
add_bars(x=~fct_reorder(Platform,Global_Sales, .desc="true"),
y=~Global_Sales,
name="Sales Amount by Platform") %>%
layout(title="Sales Amount by Platform",
xaxis = ax,
yaxis = ay
)Wie sich herausstellt ist dies jedoch nicht die Platform welches die meisten Gamereleases hat.
Nun analzsieren wir die unterschiede der Regionen.
Hierzu verwenden wir sowohl sorted bar plots sowie um einen besseren Überblick über die Prozentualverteilung der Konsolen in den einzelnen Regionen Pie charts.
Einzuwenden hierbei ist, dass die Regionen natürlich unterschiedlich viele Einwohner haben.Ein vergleich der absoluten Saleswerte ist hierbei natürlich nur wenig ausschlaggebend zeigt aber einen generellen beliebtheitstrend der Konsolen auf.
grouped <- vgsales %>%
group_by(Platform) %>%
summarize(sum(EU_Sales)) %>%
rename(
Global_Sales = "sum(EU_Sales)"
)
grouped$Global_Sales<-as_vector(grouped$Global_Sales)
ordered <- grouped[order(grouped$Global_Sales), decreasing = FALSE]
ordered$Platform <- as_factor(ordered$Platform)
ax <- list(
title = "Platform"
)
ay <- list(
title = "EU Sales (in mio)"
)
ordered%>%
plot_ly() %>%
add_bars(x=~fct_reorder(Platform,Global_Sales, .desc="true"),
y=~Global_Sales,
name="EU Sales Amount by Platform") %>%
layout(title="EU Sales Amount by Platform",
xaxis = ax,
yaxis = ay
)ordered%>%
plot_ly() %>%
add_pie(values =~Global_Sales,labels=~Platform,textinfo='label+percent',
name="EU Sales Amount by Publisher") %>%
layout(title="EU Sales Amount by Publisher",
xaxis = ax,
yaxis = ay
)grouped <- vgsales %>%
group_by(Platform) %>%
summarize(sum(NA_Sales)) %>%
rename(
Global_Sales = "sum(NA_Sales)"
)
grouped$Global_Sales<-as_vector(grouped$Global_Sales)
ordered <- grouped[order(grouped$Global_Sales), decreasing = FALSE]
ordered$Platform <- as_factor(ordered$Platform)
ax <- list(
title = "Platform"
)
ay <- list(
title = "NA Sales (in mio)"
)
ordered%>%
plot_ly() %>%
add_bars(x=~fct_reorder(Platform,Global_Sales, .desc="true"),
y=~Global_Sales,
name="NA Sales Amount by Platform") %>%
layout(title="NA Sales Amount by Platform",
xaxis = ax,
yaxis = ay
)ordered%>%
plot_ly() %>%
add_pie(values =~Global_Sales,labels=~Platform,textinfo='label+percent',
name="NA Sales Amount by Publisher") %>%
layout(title="NA Sales Amount by Publisher",
xaxis = ax,
yaxis = ay
)grouped <- vgsales %>%
group_by(Platform) %>%
summarize(sum(JP_Sales)) %>%
rename(
Global_Sales = "sum(JP_Sales)"
)
grouped$Global_Sales<-as_vector(grouped$Global_Sales)
ordered <- grouped[order(grouped$Global_Sales), decreasing = FALSE]
ordered$Platform <- as_factor(ordered$Platform)
ax <- list(
title = "Platform"
)
ay <- list(
title = "JP Sales (in mio)"
)
ordered%>%
plot_ly() %>%
add_bars(x=~fct_reorder(Platform,Global_Sales, .desc="true"),
y=~Global_Sales,
name="JP Sales Amount by Platform") %>%
layout(title="JP Sales Amount by Platform",
xaxis = ax,
yaxis = ay
)ordered%>%
plot_ly() %>%
add_pie(values =~Global_Sales,labels=~Platform,textinfo='label+percent',
name="JP Sales Amount by Platform") %>%
layout(title="JP Sales Amount by Platform",
xaxis = ax,
yaxis = ay
)Im genensatz zur EU, in der das japanische unternehmen Sony mit der PS2 und PS3 die liste anführt, hat in den USA das Landeseigene softwareunternehmen Microsoft mit der Xbox360 die Nase vorn. In Japan hingegen ist wie zu erwarten der japanische hersteller Nintendo mit dem DS der Markführer.
Um sich das Ganze etwas genauer anzuschauen betrachten wir die Anzahl der Sales per Platform im Verlauf der Jahre.
Um eine überladung des Diagramms zu verhindern werden hier allerdings nur Plattformen mit mindestens 60 mio in Sales im entsprechenden Jahr berücksichtigt. Dies hat den Nachteil das einige z.T wichtige Daten Fehlen, ein Weglassen ebenjenes Filters fürt aber zu kompletter Unübersichtlichkeit.
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colorsHier erkennen wir das Platform, wie zu erwarten, meist kurz nach Release die größten Verkaufszahlen verzeichnen.
Nun stellt sich die Frage ob sich neben bestimmten Platformen auch bestimmte Entwickler/Publisher häufen. Hierbei gehen wir davon aus das vermutlich Nintendo und EA die Liste anführen. Hierzu verwenden wir wiederum sorted Bar-Charts und kürzen die Namen um die Begriffe “Entertainment”, “Interactive”, “Game”, “Games” und “Studios” da dies die Namen nur unnötig verlängert und Filtern Publisher mit Geringen Anzahl an Game Releases.
grouped <- vgsales %>%
group_by(Publisher) %>%
summarize(Anzahl =n()) %>%
filter(Anzahl>100) %>% filter(Publisher!="Unknown")
ordered <- grouped[order(grouped$Anzahl), decreasing = FALSE]
ordered$Publisher <-str_remove_all(ordered$Publisher, "Entertainment")
ordered$Publisher <-str_remove_all(ordered$Publisher, "Interactive")
ordered$Publisher <-str_remove_all(ordered$Publisher, "Studios")
ordered$Publisher <-str_remove_all(ordered$Publisher, "Games")
ordered$Publisher <-str_remove_all(ordered$Publisher, "Game")
ordered$Publisher <- as_factor(ordered$Publisher)
ax <- list(
title = "Publisher"
)
ay <- list(
title = "Anzahl"
)
ordered%>%
plot_ly() %>%
add_bars(x=~fct_reorder(Publisher,Anzahl, .desc="true"),
y=~Anzahl,
name="Game Amount by Publisher") %>%
layout(title="Game Amount by Publisher",
xaxis = ax,
yaxis = ay
)grouped <- vgsales %>%
group_by(Publisher) %>%
summarize(Anzahl =n(),sum(Global_Sales)) %>%
filter(Anzahl>100) %>%
rename(
Global_Sales = "sum(Global_Sales)"
)
grouped$Global_Sales<-as_vector(grouped$Global_Sales)
ordered <- grouped[order(grouped$Global_Sales), decreasing = FALSE]
ordered$Publisher <-str_remove_all(ordered$Publisher, "Entertainment")
ordered$Publisher <-str_remove_all(ordered$Publisher, "Interactive")
ordered$Publisher <-str_remove_all(ordered$Publisher, "Studios")
ordered$Publisher <-str_remove_all(ordered$Publisher, "Games")
ordered$Publisher <-str_remove_all(ordered$Publisher, "Game")
ordered$Publisher <- as_factor(ordered$Publisher)
ax <- list(
title = "Publisher"
)
ay <- list(
title = "Global Sales (in mio)"
)
ordered%>%
plot_ly() %>%
add_bars(x=~fct_reorder(Publisher,Global_Sales, .desc="true"),
y=~Global_Sales,
name="Sales Amount by Publisher") %>%
layout(title="Sales Amount by Publisher",
xaxis = ax,
yaxis = ay
)Auffällig ist hier die diskrepanz zwischen Anzahl der Spielereleases und Anzahl der Spieleverkäufe. Im gegensatz zu der Anzahl der Spiele (bei dem Nintendo nur auf platz 6 Sitz) dominiert Nintendo im anzahl der Sales.
Nun stellt sich wieder einmal die Frage ob sich dies in Bestimmten teilen der Welt unterscheidet und hierzu verwenden wir wiederum die SortedBar/Piechart kombo und die bereits genannten Filter
grouped <- vgsales %>%
group_by(Publisher) %>%
summarize(Anzahl =n(),sum(EU_Sales)) %>%
filter(Anzahl>100) %>%
rename(
Global_Sales = "sum(EU_Sales)"
)
grouped$Global_Sales<-as_vector(grouped$Global_Sales)
ordered <- grouped[order(grouped$Global_Sales), decreasing = FALSE]
ordered$Publisher <-str_remove_all(ordered$Publisher, "Entertainment")
ordered$Publisher <-str_remove_all(ordered$Publisher, "Interactive")
ordered$Publisher <-str_remove_all(ordered$Publisher, "Studios")
ordered$Publisher <-str_remove_all(ordered$Publisher, "Games")
ordered$Publisher <-str_remove_all(ordered$Publisher, "Game")
ordered$Publisher <- as_factor(ordered$Publisher)
ax <- list(
title = "Publisher"
)
ay <- list(
title = "EU Sales (in mio)"
)
ordered%>%
plot_ly() %>%
add_bars(x=~fct_reorder(Publisher,Global_Sales, .desc="true"),
y=~Global_Sales,
name="EU Sales Amount by Publisher") %>%
layout(title="EU Sales Amount by Publisher",
xaxis = ax,
yaxis = ay
)ordered%>%
plot_ly() %>%
add_pie(values =~Global_Sales,labels=~Publisher,
name="EU Sales Amount by Publisher") %>%
layout(title="EU Sales Amount by Publisher",
xaxis = ax,
yaxis = ay
)grouped <- vgsales %>%
group_by(Publisher) %>%
summarize(Anzahl =n(),sum(NA_Sales)) %>%
filter(Anzahl>100) %>%
rename(
Global_Sales = "sum(NA_Sales)"
)
grouped$Global_Sales<-as_vector(grouped$Global_Sales)
ordered <- grouped[order(grouped$Global_Sales), decreasing = FALSE]
ordered$Publisher <-str_remove_all(ordered$Publisher, "Entertainment")
ordered$Publisher <-str_remove_all(ordered$Publisher, "Interactive")
ordered$Publisher <-str_remove_all(ordered$Publisher, "Studios")
ordered$Publisher <-str_remove_all(ordered$Publisher, "Games")
ordered$Publisher <-str_remove_all(ordered$Publisher, "Game")
ordered$Publisher <- as_factor(ordered$Publisher)
ax <- list(
title = "Publisher"
)
ay <- list(
title = "NA Sales (in mio)"
)
ordered%>%
plot_ly() %>%
add_bars(x=~fct_reorder(Publisher,Global_Sales, .desc="true"),
y=~Global_Sales,
name="NA Sales Amount by Publisher") %>%
layout(title="NA Sales Amount by Publisher",
xaxis = ax,
yaxis = ay
)ordered%>%
plot_ly() %>%
add_pie(values =~Global_Sales,labels=~Publisher,textinfo='label+percent',
name="NA Sales Amount by Publisher") %>%
layout(title="NA Sales Amount by Publisher",
xaxis = ax,
yaxis = ay
)grouped <- vgsales %>%
group_by(Publisher) %>%
summarize(Anzahl =n(),sum(JP_Sales)) %>%
filter(Anzahl>100) %>%
rename(
Global_Sales = "sum(JP_Sales)"
)
grouped$Global_Sales<-as_vector(grouped$Global_Sales)
ordered <- grouped[order(grouped$Global_Sales), decreasing = FALSE]
ordered$Publisher <-str_remove_all(ordered$Publisher, "Entertainment")
ordered$Publisher <-str_remove_all(ordered$Publisher, "Interactive")
ordered$Publisher <-str_remove_all(ordered$Publisher, "Studios")
ordered$Publisher <-str_remove_all(ordered$Publisher, "Games")
ordered$Publisher <-str_remove_all(ordered$Publisher, "Game")
ordered$Publisher <- as_factor(ordered$Publisher)
ax <- list(
title = "Publisher"
)
ay <- list(
title = "JP Sales (in mio)"
)
ordered%>%
plot_ly() %>%
add_bars(x=~fct_reorder(Publisher,Global_Sales, .desc="true"),
y=~Global_Sales,
name="JP Sales Amount by Publisher") %>%
layout(title="JP Sales Amount by Publisher",
xaxis = ax,
yaxis = ay
)ordered%>%
plot_ly() %>%
add_pie(values =~Global_Sales,labels=~Publisher,
name="JP Sales Amount by Publisher") %>%
layout(title="JP Sales Amount by Publisher",
xaxis = ax,
yaxis = ay
)Zwischen EU und US lassen sich hier kaum Unterschiede feststellen in Japan hingegen ist eine verschiebung zu erkennen. Japanische Publisher führen hierbei die Liste an und drängen andere große unternehmen von den Top spots.
Nun schauen wir uns die Genreverteilung von Videospielen an. Hierzu verwenden wir wiederum die Altbekannte sorted Bar/Pie-Chart kombo.
grouped <- vgsales %>%
group_by(Genre) %>%
summarize(Anzahl =n())
grouped$Anzahl<-as_vector(grouped$Anzahl)
ordered <- grouped[order(grouped$Anzahl), decreasing = FALSE]
ordered$Genre <- as_factor(ordered$Genre)
ax <- list(
title = "Genre"
)
ay <- list(
title = "Anzahl"
)
ordered%>%
plot_ly() %>%
add_bars(x=~fct_reorder(Genre,Anzahl, .desc="true"),
y=~Anzahl,
name="Amount by Genre") %>%
layout(title="Amount by Genre",
xaxis = ax,
yaxis = ay
)ordered%>%
plot_ly() %>%
add_pie(values =~Anzahl,labels=~Genre,
name="Amount by Genre") %>%
layout(title="Amount by Genre",
xaxis = ax,
yaxis = ay
)Hierbei ist zu erkennen das Action und Sports ein Großteil (34%) der Genres ausmachen. Die Frage ist jedoch wie sind die Verkäufe verteilt und beeinflusst die anzahl der Games pro Genre irgendwie die Verkaufszahlen?
grouped <- vgsales %>%
group_by(Genre) %>%
summarize(sum(Global_Sales)) %>%
rename(
Global_Sales = "sum(Global_Sales)"
)
grouped$Global_Sales<-as_vector(grouped$Global_Sales)
ordered <- grouped[order(grouped$Global_Sales), decreasing = FALSE]
ax <- list(
title = "Genre"
)
ay <- list(
title = "Sales (in mio)"
)
ordered%>%
plot_ly() %>%
add_bars(x=~fct_reorder(Genre,Global_Sales, .desc="true"),
y=~Global_Sales,
name="Sales by Genre") %>%
layout(title="Sales by Genre",
xaxis = ax,
yaxis = ay
)ordered%>%
plot_ly() %>%
add_pie(values =~Global_Sales,labels=~Genre,
name="Sales by Genre") %>%
layout(title="Sales by Genre",
xaxis = ax,
yaxis = ay
)Hierbei erkennen wir das es einige Unterschiede gibt Action und sports machen jedoch weiterhin einen Grosteil des Markts aus.
Nun schauen wir uns die Unterscheide in den Regionen an.
grouped <- vgsales %>%
group_by(Genre) %>%
summarize(sum(EU_Sales)) %>%
rename(
Global_Sales = "sum(EU_Sales)"
)
grouped$Global_Sales<-as_vector(grouped$Global_Sales)
ordered <- grouped[order(grouped$Global_Sales), decreasing = FALSE]
ax <- list(
title = "Genre"
)
ay <- list(
title = "Sales (in mio)"
)
ordered%>%
plot_ly() %>%
add_bars(x=~fct_reorder(Genre,Global_Sales, .desc="true"),
y=~Global_Sales,
name="Sales by Genre (EU)") %>%
layout(title="Sales by Genre (EU)",
xaxis = ax,
yaxis = ay
)ordered%>%
plot_ly() %>%
add_pie(values =~Global_Sales,labels=~Genre,
name="Sales by Genre (EU)") %>%
layout(title="Sales by Genre (EU)",
xaxis = ax,
yaxis = ay
)grouped <- vgsales %>%
group_by(Genre) %>%
summarize(sum(NA_Sales)) %>%
rename(
Global_Sales = "sum(NA_Sales)"
)
grouped$Global_Sales<-as_vector(grouped$Global_Sales)
ordered <- grouped[order(grouped$Global_Sales), decreasing = FALSE]
ax <- list(
title = "Genre"
)
ay <- list(
title = "Sales (in mio)"
)
ordered%>%
plot_ly() %>%
add_bars(x=~fct_reorder(Genre,Global_Sales, .desc="true"),
y=~Global_Sales,
name="Sales by Genre (NA)") %>%
layout(title="Sales by Genre (NA)",
xaxis = ax,
yaxis = ay
)ordered%>%
plot_ly() %>%
add_pie(values =~Global_Sales,labels=~Genre,
name="Sales by Genre (NA)") %>%
layout(title="Sales by Genre (NA)",
xaxis = ax,
yaxis = ay
)grouped <- vgsales %>%
group_by(Genre) %>%
summarize(sum(JP_Sales)) %>%
rename(
Global_Sales = "sum(JP_Sales)"
)
grouped$Global_Sales<-as_vector(grouped$Global_Sales)
ordered <- grouped[order(grouped$Global_Sales), decreasing = FALSE]
ax <- list(
title = "Genre"
)
ay <- list(
title = "Sales"
)
ordered%>%
plot_ly() %>%
add_bars(x=~fct_reorder(Genre,Global_Sales, .desc="true"),
y=~Global_Sales,
name="Sales by Genre (JP)") %>%
layout(title="Sales by Genre (JP)",
xaxis = ax,
yaxis = ay
)ordered%>%
plot_ly() %>%
add_pie(values =~Global_Sales,labels=~Genre,
name="Sales by Genre (JP)") %>%
layout(title="Sales by Genre (JP)",
xaxis = ax,
yaxis = ay
)Hierbei erkennen wir wieder einmal das US und EU recht ähnlich sind wohingegen Japan ein gänzlich anderes Genreshema erkennen lässt. In Japan dominiert Role-Playing welches sich in EU und US nur auf platz 7 befindet.
Nun schauen wir uns die Genreentwicklung über die Jahre an. Hierzu verwenden wir LinePlots.
grouped <- vgsales %>%
group_by(Year, Genre) %>%
dplyr::summarize(Anzahl =n()) %>% filter(Year!='N/A')%>%filter(Year!=2020) %>% filter(Year!='2017') %>%
as.data.frame()
filtered <- grouped %>% select(Year,Genre,Anzahl)
ax <- list(
title = "Year"
)
ay <- list(
title = "Anzahl"
)
filtered %>%
plot_ly() %>%
add_lines(x=~Year,
y=~Anzahl, color=~Genre)## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colorsfiltered %>%
plot_ly(x = ~Year, y = ~Anzahl, type = 'scatter', mode = 'none', fill = 'tozeroy',color = ~Genre)%>%
layout(title="Amount by Genre from 1980-2016",
xaxis = ax,
yaxis = ay
)## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colorsfiltered %>%
plot_ly(x = ~Year, y = ~Anzahl, type = 'scatter', mode = 'none', stackgroup = 'one',color = ~Genre)%>%
layout(title="Amount by Genre from 1980-2016",
xaxis = ax,
yaxis = ay
)## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colorsay <- list(
title = "Percent %"
)
filtered %>%
plot_ly(x = ~Year, y = ~Anzahl, type = 'scatter', mode = 'none', stackgroup = 'one',groupnorm = 'percent',color = ~Genre)%>%
layout(title="Marketamount genreshift in % from 1980-2016",
xaxis = ax,
yaxis = ay
)## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colorsSchauen wir uns an wie sich die verkäufe entwickelt haben.
grouped <- vgsales %>%
group_by(Year, Genre) %>%
dplyr::summarize(gr_sum = sum(Global_Sales)) %>% filter(Year!='N/A')%>%filter(Year!=2020) %>% filter(Year!='2017') %>%
as.data.frame()
filtered <- grouped %>% select(Year,Genre,gr_sum)
ax <- list(
title = "Year"
)
ay <- list(
title = "Global_Sales"
)
filtered %>%
plot_ly() %>%
add_lines(x=~Year,
y=~gr_sum, color=~Genre) %>%
layout(title="Sales by Genre from 1980-2016",
xaxis = ax,
yaxis = ay
)## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colorsfiltered %>%
plot_ly(x = ~Year, y = ~gr_sum, type = 'scatter', mode = 'none', fill = 'tozeroy',color = ~Genre) %>%
layout(title="Sales by Genre from 1980-2016",
xaxis = ax,
yaxis = ay
)## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colorsfiltered %>%
plot_ly(x = ~Year, y = ~gr_sum, type = 'scatter', mode = 'none', stackgroup = 'one',color = ~Genre)%>%
layout(title="Sales by Genre from 1980-2016",
xaxis = ax,
yaxis = ay
)## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colorsay <- list(
title = "Percent %"
)
filtered %>%
plot_ly(x = ~Year, y = ~gr_sum, type = 'scatter', mode = 'none', stackgroup = 'one',groupnorm = 'percent',color = ~Genre)%>%
layout(title="Marketshare genreshift in % from 1980-2016",
xaxis = ax,
yaxis = ay
)## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colorsNun stellt sich die Frage hab sich die Verkäufe ähnlich der anzahl der GameReleases entwickelt?
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colorsWie sieht es mit Genres pro publisher aus?
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colorsWie sieht es mit den Pro kopf Sales Global und pro Region aus?